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techniques. We are a community based on mutual support and collaboration. Through our Doctoral College there are continual opportunities for building important research skills and networking among your peers
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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technological advances that support the global transition toward net-zero emissions and sustainable aerospace engineering. Motivation The reliability of electric propulsion systems is pivotal for next-generation
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challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make technological advances that support the global transition toward net-zero
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models considering networks of patches and their species and interactions composition to predict spatial and temporal community structure across restoration gradients, aimed at developing a predictive
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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Cyber-Physical Power Systems Research area and project description: This is an exciting PhD project to be carried out in close collaboration with a UK electrical network operator and an industrial co
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construction of creek networks to ensure the transfer of water, sediment and nutrients around the site. However, the current approach to site design involves the use of pre-existing agricultural drainage ditches
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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topics in their nation of residence. Professor Seaborn is lead of and a listed supporter on the IDE+A Network: https://the-idea.network Click the 'Apply' button below to register an account with our